- Exactly one replica is designated the master. This task manages the others and reports status for the job as a whole. The training service runs until your job succeeds or encounters an unrecoverable error. In the distributed case, it is the status of the master replica that signals the overall job status.
- If you are running a single-process job, the sole replica is the master for the job.
- One or more replicas may be designated as workers. These replicas do their portion of the work as you designate in your job configuration.
- One or more replicas may be designated as parameter servers. These replicas coordinate shared model state between the workers.
For more on the distributed training flow, see https://cloud.google.com/ml-engine/docs/tensorflow/distributed-tensorflow-mnist-cloud-datalab